Parameter Tuning Strategies for Large Language Models

⚙️ Fine-Tuning Large Language Models: What Works, When, and Why Fine-tuning is not a magic wand. It's a design decision balancing specificity and generality, control and cost, performance and pragmatism. Let's break down the engineering tradeoffs. 🔧 1. Full Fine-Tuning Full Fine Tuning updates all model weights, offering the best performance but at the highest cost and lowest modularity. When to use: → High-stakes domains (medical, legal, aerospace) → When training data diverges from pre-trained distribution → When interpretability matters more than generality Pros: ✅ State-of-the-art performance in specialized domains ✅ Complete behavioral control—no surprises ✅ Enables deep internal shifts in model representations Cons: ⚠️ Requires 3-4x the base model's memory during training ⚠️ High risk of catastrophic forgetting ⚠️ Unwieldy checkpoints (dozens of GBs) ⚠️ Computationally intensive 🧠 2. Parameter-Efficient Fine-Tuning (PEFT) PEFT adds minimal learnable components into a frozen pre-trained model. A. LoRA (Low-Rank Adaptation) LoRA introduces low-rank matrices into specific layers, achieving high efficiency and performance close to full fine tuning, with no inference overhead after merging. Why it works: Transformer weights are often over-parameterized. Low-rank deltas steer behavior without disrupting the base. Pros: ✅ Trains just ~0.2% of parameters ✅ Reduces cost by 70-80% ✅ Works with off-the-shelf models ✅ Compatible with consumer GPUs (16-24GB VRAM) Cons: ⚠️ Slight performance dip for outlier tasks ⚠️ Managing multiple adapters increases complexity B. Adapters Adapters add small modules between layers, providing modularity and efficiency, but with a minor inference cost since adapters remain in the model. Why it works: Creates isolated "learning compartments" letting you swap behaviors without retraining. Pros: ✅ Strong modularity for multi-task settings ✅ Easier governance: version and audit per adapter ✅ Widely supported in open-source Cons: ⚠️ Increased inference latency ⚠️ Requires architectural support C. Prefix Tuning Prefix Tuning adds trainable vectors to the model’s input or transformer blocks, making it the most parameter-efficient and fastest to train, but generally with lower performance on complex tasks and best for scenarios where preserving the pre-trained model’s representation is critical Why it works: Initial LLM layers are sensitive to context. Prefix vectors steer activations like tuning a radio. Pros: ✅ Trains <0.1% of parameters ✅ Fast training and inference ✅ Ideal for personalization and low-resource devices Cons: ⚠️ Less stable in models >30B unless regularized ⚠️ Struggles with deep reasoning tasks In 2025, switch from "Can I fine-tune?" to "What am I optimizing for?" If you need control? Full fine-tuning- at a cost. If you need agility? LoRA or adapters. If you need speed? Prefix tuning. Share it with your network ♻️ Follow me(Aishwarya Srinivasan) for more no-fluff AI insights

Our AI Lab has a new breakthrough: Finetuning quantized LLMs Quantization has made it much easier to run LLMs, shrinking them to INT8/INT4 so they can work on more accessible hardware. Yet, once quantized, these models are incredibly hard to improve. Small updates vanish in discrete parameter spaces, making fine-tuning impractical. With our latest work at the Cognizant AI Lab, Quantized Evolution Strategies (QES) address exactly that. QES enables full-parameter fine-tuning directly in quantized space — without backpropagation — using a zeroth-order optimization approach. And importantly, it does this at roughly the same memory cost as inference! We introduced techniques like accumulated error feedback and stateless seed replay to preserve learning signals that would otherwise be lost. The final outcome shows strong improvements in reasoning performance, even for highly compressed models. This means if a system can run a quantized model, it can now fine-tune it on the same hardware. This opens up new possibilities for adapting and customizing models post-deployment, without requiring heavy infrastructure. Read the blog: https://cgnz.at/6007QofEf Read the paper: https://cgnz.at/6002Qof1p #AI #MachineLearning #LLM #Quantization #EvolutionStrategies #AIResearch

Understanding LLM Adaptation: Full Training vs Fine-Tuning vs LoRA/QLoRA 🚀 Which approach really moves the needle in today’s AI landscape? As large language models (LLMs) become mainstream, I frequently get asked: “Should we train from scratch, full fine-tune, or use LoRA/QLoRA adapters for our use case?” Here’s a simple breakdown based on real-world considerations: 🔍 1. Full Training from Scratch What: Building a model from the ground up with billions of parameters. Who: Only major labs/Big Tech (OpenAI, Google, etc.) Cost: 🏦 Millions—requires massive clusters and huge datasets. Why: Needed ONLY if you want a truly unique model architecture or foundation. 🛠️ 2. Full Fine-Tuning What: Take an existing giant model and update ALL its weights for your task. Who: Advanced companies with deep pockets. Cost: 💰 Tens of thousands to millions—need multiple high-end GPUs. Why: Useful if you have vast domain data and need to drastically “re-train” the model’s capabilities. ⚡ 3. LoRA/QLoRA (Parameter-Efficient Tuning) What: Plug low-rank adapters into a model, updating just 0.5-5% of weights. Who: Startups, researchers, almost anyone! Cost: 💡 From free (on Google Colab) to a few hundred dollars on cloud GPUs. Why: Customize powerful LLMs efficiently—think domain adaption, brand voice, or private datasets, all without losing the model’s general smarts. 🤔 Which one should YOU use? For most organizations and projects, LoRA/QLoRA is the optimal sweet spot Fast: Results in hours, not weeks Affordable: Accessible to almost anyone Flexible: Update or revert adapters with ease Full fine-tuning and from-scratch training make sense only for the biggest players—99% of AI innovation today leverages parameter-efficient tuning! 💬 What’s your experience? Are you using full fine-tunes, or has LoRA/QLoRA met your business needs? Share your project (or frustrations!) in comments.

Confession: At a conference a few months ago, I ended up in a conversation with two engineers who kept talking about “LoRA” and “DORA.” I genuinely thought they were referring to founders. Like… Laura and Dora. Maybe they’d raised a big round? Spoiler: they’re not people. They’re fine-tuning methods. That night, I sat on my bed typing: “LoRA DORA AI explained like I’m five and socially humiliated” Anyway, welcome to Day 29 of learning about AI in public. A few years ago, if you wanted to fine-tune a large language model, you needed millions of dollars, hundreds of GPUs, and a serious infrastructure team. Today? Thanks to a class of methods called parameter-efficient fine-tuning, you can do it with a single GPU. Let’s break down how. “Full fine-tuning” means updating all of a model’s internal settings (parameters) to adapt it to a new task. That’s what big labs used to do. But it has big downsides: - Training a 65 billion–parameter model requires clusters of GPUs, each costing thousands of dollars - Just loading a 16-bit version of the model requires over 130GB of VRAM - Every experiment means re-training from scratch - If your dataset is small or narrow, the model might “memorize” instead of learning useful generalizations PEFT flips this: it freezes most of the model and only updates a small, smart subset of parameters. Part 1: Low-Rank Adaptation LoRA’s idea is simple: instead of updating an entire matrix of weights, just learn a small patch that captures what’s different about your new task. Take the pretrained model. Don’t touch its main weights. Insert two small matrices (A and B) inside certain layers, typically the attention and feedforward layers These matrices learn a low-rank approximation of the change you would have made Think of it like adding removable “task-specific plugins” to a general-purpose brain. Part 2: Quantized LoRA LoRA is great but it still assumes you can fit the base model in memory. That’s where QLoRA comes in: - It compresses the base model into 4-bit precision - Keeps LoRA adapters in normal precision - Uses tricks like offloading optimizer states to your CPU - Still allows backpropagation (i.e., learning) through the adapter The result? You can fine-tune a 65B model on a single consumer-grade 48GB GPU. And you lose almost no performance. Part 3: Dynamic Rank Adaptation LoRA requires you to set a “rank” (basically: how big your low-rank patch is). QLoRA keeps that fixed. But different tasks and different model layers need different levels of expressiveness. DORA fixes that: - It lets each layer learn its own optimal rank automatically - Uses smart techniques (like matrix decomposition) to identify which layers need more or less capacity - Keeps performance high while reducing waste In a nutshell: PEFT makes LLM customization democratized, cheap, and modular. 👉 This is Day 29 of my 30-day deep dive into AI. Follow Justine Juillard to go from AI-curious to AI-confident.

So, you’ve got a big, powerful Large Language Model (LLM). It’s smart, but it’s not your kind of smart. You want it to write like you, code like your team, or understand the jargon of your industry. The old way to do this was full fine-tuning. Imagine you have a master chef (your base LLM). To teach them a new recipe, full fine-tuning is like making them go through culinary school all over again. It takes months, costs a fortune, and you need a kitchen the size of a stadium (read: a multi-GPU server). It’s slow, expensive, and frankly, overkill. But what if you could just pass the chef a recipe card? What if you could make a few, tiny, expert-level adjustments to get the exact flavour you want? That's Parameter-Efficient Fine-Tuning (PEFT). PEFT lets you achieve 95% of the performance of a full fine-tune while training less than 1% of the parameters. In this comprehensive guide or tutorial if you will, I break down: • The complete LoRA workflow • QLoRA for training 70B models on consumer hardware • DoRA - the newest method that outperforms standard LoRA • AdaLoRA’s adaptive parameter allocation • IA³ for ultra-efficient fine-tuning • A decision framework to choose the right method The barrier to AI customization has essentially disappeared. Startups can now compete with tech giants using weekend hardware. Link to the full premium guide in comments 👇 What’s been your experience with PEFT methods?

≠ Not all fine-tuning techniques are equal. Some cost next to nothing, others… not so much. Fine-tuning is the process of taking a pre-trained model and further training it on a smaller, specific dataset to adapt it for a new task or on a new domain. It’s the best way to get the most value out of a LLM, but navigating the fine-tuning landscape can be tricky. Here’s a high-level overview of a few different options. The first is full fine-tuning (the most intense option), the other two are popular PEFT (parameter efficient fine-tuning) methods. 🐘 Full fine-tuning: A training method that updates all of a pre-trained model's parameters on a new, task-specific dataset, adapting the entire model to the new task. ✦ Ideal for: When you need a highly performance model on a new, highly specific task. ✦ Compute needed: Big ole GPU cluster. 💸 🔌 LoRA (Low-Rank Adaptation): A parameter-efficient fine-tuning method that adapts a large pre-trained model for a new task by training small, new matrices (adapters) while keeping the original model weights frozen. ✦ Ideal for: Adapting a model’s existing knowledge to multiple tasks. ✦ Compute needed: Pro-level GPU (e.g. A100, H100) with ample VRAM. 🤏 QLoRA (Quantized Low-Rank Adaptation): A more memory-efficient version of LoRA that performs the fine-tuning process on a quantized, low-precision version of the base model, allowing massive models to be trained on consumer GPUs. ✦ Ideal for: Prototyping and experimentation on a budget. ✦ Compute needed: Single GPU (perhaps with limited VRAM). Check out the papers for a more nuanced take. 📄 LoRA Paper: https://lnkd.in/gv_Z9Ndu 📄 QLoRA Paper: https://lnkd.in/gsjsg9CP

Evolution strategies at scale: A new way to fine tune LLMs For a while now, reinforcement learning has been the standard way to fine tune LLMs. It works in the action space, i.e. exploring alternative actions to maximize reward, which is difficult especially with long action sequences. If we could explore LLM parameters directly, it might be possible to find more systematic and creative changes. Indeed parameter-space fine tuning is possible, and in a surprising way: evolution strategies, i.e. population-based search, can be scaled up to optimize billions of parameters in LLMs. It outperforms PPO and GRPO in a symbolic reasoning task, for example, while being more consistent and sample-efficient. Its gradient-free nature makes it a promising alternative in environments where RL is fragile or costly to apply. Read the full paper: https://lnkd.in/gGtyddty Read the blog (with a video and a conceptual illustration): https://lnkd.in/gZtWccrJ Explore the code: https://lnkd.in/gtJ95Nvb #AIResearch #EvolutionStrategies #LLM

3 Ways to Fine-Tune an AI Model 👇 Fine-tuning involves adapting a model to a particular use case through additional training. Before doing this, however, we must ask ourselves a fundamental question: which parameters do I want to fine-tune? While there are countless ways to do this, I like to split them into 3 buckets. Bucket 1: All Parameters 🌎 The simplest approach is to retrain all the parameters in your base model, also called full parameter fine-tuning. This works well when you have a lot of data and want to make fundamental changes to the model’s behavior. The downside, of course, is that this is the most computationally expensive option and risks the model “forgetting” key abilities. Bucket 2: Some Parameters 🪛 To mitigate the compute requirements and training risks of full fine-tuning, one can pick a subset of parameters to retrain. This is called transfer learning, which typically consists of retraining the last few layers of a model (and maybe adding a new one). The key upside of this is that you can significantly improve a model's performance on a novel task without major data and compute costs. However, even this approach can be impractical for very large base models. Bucket 3: New Parameters (i.e. adapters) 💻 This final bucket consists of adding a (relatively) small number of trainable parameters to key parts of a model while keeping its original parameters frozen. The technical term for this approach is Parameter Efficient Fine-Tuning (PEFT). A popular PEFT approach is Low-Rank Adaption (LoRA), which has made fine-tuning large language models practical for GPU-poor practitioners. Additionally, this is a handy approach when working with a relatively small training data set (which is the biggest unlock IMO).

Choosing the right fine-tuning method can save you weeks in time and thousands in compute. By now, most people know that you need to fine-tune language models to get the most value out of them, but what many don’t know is which fine-tuning approach to use. And there are several. So here’s a brief overview of some of the most popular fine-tuning techniques and when to use them. Full Fine-Tuning: Retrains the entire model on your data. • Best for: Critical applications where you need maximum performance and have very specialized needs. • Cost: High. You’ll likely need a GPU cluster. • Risk: Huge waste of money if you don't actually need this level of customization. LoRA: Trains small adapter layers while keeping the base model frozen. • Best for: Running one model across multiple tasks without starting from scratch each time. • Cost: Moderate. Needs good GPUs (A100/H100 range). • Risk: Might not be enough for highly regulated or niche domains. QLoRA: LoRA but on a compressed model. • Best for: Testing ideas fast or working with limited resources. • Cost: Low. Runs on regular GPUs. • Risk: The compression can hurt performance, making it risky for production. One particular workflow that works well is starting with QLoRA to validate your approach and move to LoRA when you're scaling and reserving full fine-tuning only if model accuracy is very important and you have the time and budget.

If you fine-tune large language model without understanding LoRA’s hyperparameters, you’re likely wasting compute, time, or both. LoRA fine-tuning is controlled by three hyperparameters that balance expressiveness, training stability, and computational cost. Rank R sets the dimensionality of adapter matrices. Values as low as 4-8-16 can match full fine-tuning performance, with 8 being the most common starting point. The choice is primarily constrained by available memory rather than model quality. Alpha acts as a scaling factor, typically set to 2×R, that maintains training stability when experimenting with different rank values. Without it, changing R would require manually adjusting learning rates each time. Target modules specify which neural network components get adapted. The default recommendation is the query and value projection matrices in attention blocks. This video is part of our 𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 program. 𝗟𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/gM7jrinp Follow for more practical insights into real-world AI engineering and development.